Codebook for processed data

Add data labels, metadata, codebook, distributions, and convert to other file types

Codebook

# Following codebook package's [vignette](https://cran.r-project.org/web/packages/codebook/vignettes/codebook_tutorial.html)

# Dependencies

library(tidyverse)
library(knitr)
library(kableExtra)
library(codebook)
library(labelled)
library(rio)

options(knitr.kable.NA = "/")


# Add labels ----

# read data
codebook_data <- read.csv("processed/4_data_participant_level_with_hand_scoring.csv")

# read data dictionary
dict <- read.csv("processed/5_data_dictionary.csv")

# add dictionary as labels
var_label(codebook_data) <- dict %>% 
  select(variable, label) %>% 
  dict_to_list()


# Add meta data ----

metadata(codebook_data)$name <- "Evaluative learning via deepfaked media"
metadata(codebook_data)$description <- "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions."
#metadata(codebook_data)$identifier <- "https://dx.doi.org/XXXXXXX"

metadata(codebook_data)$creator <- "Sean Hughes"
metadata(codebook_data)$citation <- "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind."
metadata(codebook_data)$url <- "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-"

# other meta data:  see https://schema.org/Dataset
metadata(codebook_data)$datePublished <- "2020"
metadata(codebook_data)$spatialCoverage <- "Online" 


# Create codebook ----

codebook(codebook_data)

Metadata

Description

Dataset name: Evaluative learning via deepfaked media

Across multiple experiments, we demonstrated that ‘deepfakes’ can establish automatic biases, self-reported evaluations, and behavioural intentions.

Metadata for search engines

• Spatial Coverage: Online

• Citation: Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind.

• URL: https://github.com/Sean-Hughes/DF-Impression-Formation–Video-and-Audio-

• Date published: 2020

• Creator:

  name	value
  1	Sean Hughes

x subject experiment intervention_medium source_valence experiment_condition exclude_subject age gender IAT_D2 mean_self_reported_evaluation mean_intentions deepfake_detected diagnosticity demand reactance hypothesis_awareness influence_awareness aot_actively_openminded_thinking_sum bcti_belief_in_conspiracy_sum crt_analytic_thinking_sum ocq_overclaiming_sum ras_relgious_affliation_sum rei_rational_sum rei_experiential_sum me_fake_news_awareness_sum me_real_news_awareness_sum me_fake_news_accuracy_sum me_real_news_accuracy_sum me_fake_news_sharing_sum me_real_news_sharing_sum aot_actively_openminded_thinking_pomp bcti_belief_in_conspiracy_pomp crt_analytic_thinking_pomp ocq_overclaiming_pomp ras_relgious_affliation_pomp rei_rational_pomp rei_experiential_pomp me_fake_news_awareness_pomp me_real_news_awareness_pomp me_fake_news_accuracy_pomp me_real_news_accuracy_pomp me_fake_news_sharing_pomp me_real_news_sharing_pomp IAT_D2_recoded_for_source_valence mean_self_reported_evaluation_recoded_for_source_valence mean_intentions_recoded_for_source_valence deepfake_concept_check deepfake_detected_rater_1 deepfake_detected_rater_2 deepfake_concept_check_rater_1 deepfake_concept_check_rater_2 gender_self_describe ethnicity ethnicity_self_describe location education education_recoded employment income income_recoded political_ideology_identity political_ideology_social_issues political_ideology_economic_issues religious_affiliation_general religious_affiliation_general_recoded passed_iat_performance complete_iat complete_selfreport complete_intentions task_order iat_block_order

#Variables

subject

Unique subject identifier

Distribution

Distribution of values for subject

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
subject	Unique subject identifier	character	0	1	1730	0	36	36	0

experiment

Experiement data was collected as part of

Distribution

Distribution of values for experiment

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
experiment	Experiement data was collected as part of	numeric	0	1	1	4	6	3.739306	1.482387	▆▇▇▅▅

intervention_medium

What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio)

Distribution

Distribution of values for intervention_medium

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
intervention_medium	What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio)	character	0	1	2	0	5	5	0

source_valence

What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages?

Distribution

Distribution of values for source_valence

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
source_valence	What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages?	character	0	1	2	0	8	8	0

experiment_condition

Was the intervention genuine or deepfaked content?

Distribution

Distribution of values for experiment_condition

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
experiment_condition	Was the intervention genuine or deepfaked content?	character	0	1	2	0	7	9	0

exclude_subject

Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions

Distribution

Distribution of values for exclude_subject

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
exclude_subject	Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions	logical	0	1	FAL: 1421, TRU: 309	0.1786127

age

Participant age

Distribution

Distribution of values for age

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
age	Participant age	numeric	0	1	18	29	70	31.24335	9.94172	▇▆▃▁▁

gender

Participant gender

Distribution

Distribution of values for gender

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
gender	Participant gender	character	0	1	5	0	4	22	0

IAT_D2

The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks

Distribution

Distribution of values for IAT_D2

141 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
IAT_D2	The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks	numeric	141	0.9184971	-1.2	0.23	1.6	0.2084644	0.3642037	▁▃▇▃▁

mean_self_reported_evaluation

Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention.

Distribution

Distribution of values for mean_self_reported_evaluation

98 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_self_reported_evaluation	Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention.	numeric	98	0.9433526	-3	0	3	-0.0859804	2.029025	▇▃▃▅▇

mean_intentions

Mean behavioural intentions to XXXXXX

Distribution

Distribution of values for mean_intentions

1485 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_intentions	Mean behavioural intentions to XXXXXX	numeric	1485	0.1416185	-2	-1	2	-0.9239184	1.052152	▇▅▃▂▁

deepfake_detected

Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.

Distribution

Distribution of values for deepfake_detected

1120 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_detected	Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.	logical	1120	0.3526012	FAL: 493, TRU: 117	0.1918033

diagnosticity

XXXXXXX

Distribution

Distribution of values for diagnosticity

70 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
diagnosticity	XXXXXXX	numeric	70	0.9595376	0	2	3	1.966867	0.7643258	▁▃▁▇▃

demand

XXXXXXX

Distribution

Distribution of values for demand

131 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
demand	XXXXXXX	logical	131	0.9242775	FAL: 1585, TRU: 14	0.0087555

reactance

XXXXXXX

Distribution

Distribution of values for reactance

131 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
reactance	XXXXXXX	logical	131	0.9242775	FAL: 1537, TRU: 62	0.0387742

hypothesis_awareness

XXXXXXX

Distribution

Distribution of values for hypothesis_awareness

1672 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
hypothesis_awareness	XXXXXXX	logical	1672	0.033526	FAL: 58	0

influence_awareness

XXXXXXX

Distribution

Distribution of values for influence_awareness

946 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
influence_awareness	XXXXXXX	logical	946	0.4531792	TRU: 697, FAL: 87	0.8890306

aot_actively_openminded_thinking_sum

Distribution

Distribution of values for aot_actively_openminded_thinking_sum

1486 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
aot_actively_openminded_thinking_sum	numeric	1486	0.1410405	22	38	48	37.40164	5.934706	▂▅▇▇▆	/

bcti_belief_in_conspiracy_sum

Distribution

Distribution of values for bcti_belief_in_conspiracy_sum

1481 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
bcti_belief_in_conspiracy_sum	numeric	1481	0.1439306	15	51	129	54.04819	23.60011	▇▇▆▂▁	/

crt_analytic_thinking_sum

Distribution

Distribution of values for crt_analytic_thinking_sum

1236 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
crt_analytic_thinking_sum	numeric	1236	0.2855491	0	4	7	3.953441	1.848155	▃▃▇▅▆	/

ocq_overclaiming_sum

Distribution

Distribution of values for ocq_overclaiming_sum

1480 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
ocq_overclaiming_sum	numeric	1480	0.1445087	10	72	143	71.516	29.66252	▃▇▇▆▂	/

ras_relgious_affliation_sum

Distribution

Distribution of values for ras_relgious_affliation_sum

1194 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
ras_relgious_affliation_sum	numeric	1194	0.3098266	8	21	40	20.77799	8.32119	▇▆▇▅▂	/

rei_rational_sum

Distribution

Distribution of values for rei_rational_sum

1235 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
rei_rational_sum	numeric	1235	0.2861272	15	52	70	50.79394	10.68324	▁▂▅▇▅	/

rei_experiential_sum

Distribution

Distribution of values for rei_experiential_sum

1235 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
rei_experiential_sum	numeric	1235	0.2861272	16	49	70	48.4101	8.768012	▁▂▇▇▂	/

me_fake_news_awareness_sum

XXXXXXX

Distribution

Distribution of values for me_fake_news_awareness_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_awareness_sum	XXXXXXX	numeric	1486	0.1410405	0	1	6	1.413934	1.191866	▇▃▂▁▁

me_real_news_awareness_sum

XXXXXXX

Distribution

Distribution of values for me_real_news_awareness_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_awareness_sum	XXXXXXX	numeric	1486	0.1410405	0	0	4	0.2008197	0.5178296	▇▁▁▁▁

me_fake_news_accuracy_sum

XXXXXXX

Distribution

Distribution of values for me_fake_news_accuracy_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_accuracy_sum	XXXXXXX	numeric	1486	0.1410405	7	17	23	16.72131	2.614497	▁▂▇▇▂

me_real_news_accuracy_sum

XXXXXXX

Distribution

Distribution of values for me_real_news_accuracy_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_accuracy_sum	XXXXXXX	numeric	1486	0.1410405	6	9	20	9.127049	2.460437	▇▆▂▁▁

me_fake_news_sharing_sum

XXXXXXX

Distribution

Distribution of values for me_fake_news_sharing_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_sharing_sum	XXXXXXX	numeric	1486	0.1410405	0	1	6	1.561475	1.768585	▇▂▁▂▁

me_real_news_sharing_sum

XXXXXXX

Distribution

Distribution of values for me_real_news_sharing_sum

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_sharing_sum	XXXXXXX	numeric	1486	0.1410405	0	0	6	0.454918	0.9739489	▇▁▁▁▁

aot_actively_openminded_thinking_pomp

XXXXXXX

Distribution

Distribution of values for aot_actively_openminded_thinking_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
aot_actively_openminded_thinking_pomp	XXXXXXX	numeric	1486	0.1410405	19	69	100	66.90984	18.57739	▂▅▇▇▆

bcti_belief_in_conspiracy_pomp

XXXXXXX

Distribution

Distribution of values for bcti_belief_in_conspiracy_pomp

1481 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
bcti_belief_in_conspiracy_pomp	XXXXXXX	numeric	1481	0.1439306	0	30	95	32.56627	19.67961	▇▇▆▂▁

crt_analytic_thinking_pomp

XXXXXXX

Distribution

Distribution of values for crt_analytic_thinking_pomp

1236 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
crt_analytic_thinking_pomp	XXXXXXX	numeric	1236	0.2855491	0	57	100	56.47368	26.39254	▃▃▇▅▆

ocq_overclaiming_pomp

XXXXXXX

Distribution

Distribution of values for ocq_overclaiming_pomp

1480 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
ocq_overclaiming_pomp	XXXXXXX	numeric	1480	0.1445087	6	40	79	39.728	16.45628	▃▇▇▆▂

ras_relgious_affliation_pomp

XXXXXXX

Distribution

Distribution of values for ras_relgious_affliation_pomp

1194 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
ras_relgious_affliation_pomp	XXXXXXX	numeric	1194	0.3098266	0	41	100	39.91978	26.0168	▇▆▇▅▂

rei_rational_pomp

XXXXXXX

Distribution

Distribution of values for rei_rational_pomp

1235 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
rei_rational_pomp	XXXXXXX	numeric	1235	0.2861272	-4	27	42	25.67879	8.894561	▁▂▅▇▅

rei_experiential_pomp

XXXXXXX

Distribution

Distribution of values for rei_experiential_pomp

1235 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
rei_experiential_pomp	XXXXXXX	numeric	1235	0.2861272	-3	24	42	23.68687	7.323976	▁▂▇▇▂

me_fake_news_awareness_pomp

XXXXXXX

Distribution

Distribution of values for me_fake_news_awareness_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_awareness_pomp	XXXXXXX	numeric	1486	0.1410405	0	17	100	23.62295	19.8172	▇▃▂▁▁

me_real_news_awareness_pomp

XXXXXXX

Distribution

Distribution of values for me_real_news_awareness_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_awareness_pomp	XXXXXXX	numeric	1486	0.1410405	0	0	67	3.389344	8.694166	▇▁▁▁▁

me_fake_news_accuracy_pomp

XXXXXXX

Distribution

Distribution of values for me_fake_news_accuracy_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_accuracy_pomp	XXXXXXX	numeric	1486	0.1410405	6	61	94	59.59426	14.52695	▁▂▇▇▂

me_real_news_accuracy_pomp

XXXXXXX

Distribution

Distribution of values for me_real_news_accuracy_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_accuracy_pomp	XXXXXXX	numeric	1486	0.1410405	0	17	78	17.43443	13.57618	▇▆▂▁▁

me_fake_news_sharing_pomp

XXXXXXX

Distribution

Distribution of values for me_fake_news_sharing_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_fake_news_sharing_pomp	XXXXXXX	numeric	1486	0.1410405	0	17	100	26.03279	29.46922	▇▂▁▂▁

me_real_news_sharing_pomp

XXXXXXX

Distribution

Distribution of values for me_real_news_sharing_pomp

1486 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
me_real_news_sharing_pomp	XXXXXXX	numeric	1486	0.1410405	0	0	100	7.602459	16.21796	▇▁▁▁▁

IAT_D2_recoded_for_source_valence

IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2.

Distribution

Distribution of values for IAT_D2_recoded_for_source_valence

141 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
IAT_D2_recoded_for_source_valence	IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2.	numeric	141	0.9184971	-0.94	0.2	1.6	0.1854097	0.3764714	▁▅▇▃▁

mean_self_reported_evaluation_recoded_for_source_valence

mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.

Distribution

Distribution of values for mean_self_reported_evaluation_recoded_for_source_valence

98 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_self_reported_evaluation_recoded_for_source_valence	mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.	numeric	98	0.9433526	-3	2	3	1.568836	1.289025	▁▁▂▃▇

mean_intentions_recoded_for_source_valence

mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions.

Distribution

Distribution of values for mean_intentions_recoded_for_source_valence

1485 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_intentions_recoded_for_source_valence	mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions.	numeric	1485	0.1416185	-2	0.67	2	0.5263673	1.298441	▂▃▅▂▇

deepfake_concept_check

XXXXXXX

Distribution

Distribution of values for deepfake_concept_check

1294 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_concept_check	XXXXXXX	logical	1294	0.2520231	TRU: 234, FAL: 202	0.5366972

deepfake_detected_rater_1

XXXXXXX

Distribution

Distribution of values for deepfake_detected_rater_1

1120 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_detected_rater_1	XXXXXXX	logical	1120	0.3526012	FAL: 462, TRU: 148	0.242623

deepfake_detected_rater_2

XXXXXXX

Distribution

Distribution of values for deepfake_detected_rater_2

1120 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_detected_rater_2	XXXXXXX	logical	1120	0.3526012	FAL: 476, TRU: 134	0.2196721

deepfake_concept_check_rater_1

XXXXXXX

Distribution

Distribution of values for deepfake_concept_check_rater_1

1294 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_concept_check_rater_1	XXXXXXX	logical	1294	0.2520231	TRU: 247, FAL: 189	0.5665138

deepfake_concept_check_rater_2

XXXXXXX

Distribution

Distribution of values for deepfake_concept_check_rater_2

1294 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
deepfake_concept_check_rater_2	XXXXXXX	logical	1294	0.2520231	TRU: 247, FAL: 189	0.5665138

gender_self_describe

Open ended gender

Distribution

Distribution of values for gender_self_describe

1725 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
gender_self_describe	Open ended gender	character	1725	0.0028902	1	0	4	4	0

ethnicity

Ethnicity

Distribution

Distribution of values for ethnicity

1189 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ethnicity	Ethnicity	character	1189	0.3127168	7	0	5	25	0

ethnicity_self_describe

Open ended ethnicity

Distribution

Distribution of values for ethnicity_self_describe

1701 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ethnicity_self_describe	Open ended ethnicity	character	1701	0.016763	19	0	4	40	0

location

Location

Distribution

Distribution of values for location

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
location	Location	character	1190	0.3121387	18	0	5	24	0

education

Education

Distribution

Distribution of values for education

1189 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
education	Education	character	1189	0.3127168	8	0	7	21	0

education_recoded

Education recoded into XXXXXXXX groups

Distribution

Distribution of values for education_recoded

1189 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
education_recoded	Education recoded into XXXXXXXX groups	numeric	1189	0.3127168	1	5	7	4.072089	1.526427	▅▅▁▇▃

employment

Employment status

Distribution

Distribution of values for employment

1189 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
employment	Employment status	character	1189	0.3127168	10	0	7	24	0

income

Income level

Distribution

Distribution of values for income

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
income	Income level	character	1190	0.3121387	10	0	12	20	0

income_recoded

Income level recoded into XXXXX groups

Distribution

Distribution of values for income_recoded

1238 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
income_recoded	Income level recoded into XXXXX groups	numeric	1238	0.2843931	1	3	8	2.943089	1.503835	▇▆▇▁▁

political_ideology_identity

XXXXXXX

Distribution

Distribution of values for political_ideology_identity

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
political_ideology_identity	XXXXXXX	numeric	1190	0.3121387	-3	1	3	0.8574074	1.544648	▂▂▃▇▇

political_ideology_social_issues

XXXXXXX

Distribution

Distribution of values for political_ideology_social_issues

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
political_ideology_social_issues	XXXXXXX	numeric	1190	0.3121387	-2	-1	2	-0.637037	1.023707	▅▇▆▂▁

political_ideology_economic_issues

XXXXXXX

Distribution

Distribution of values for political_ideology_economic_issues

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
political_ideology_economic_issues	XXXXXXX	numeric	1190	0.3121387	-2	0	2	-0.4148148	1.032929	▃▇▇▃▁

religious_affiliation_general

XXXXXXX

Distribution

Distribution of values for religious_affiliation_general

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
religious_affiliation_general	XXXXXXX	character	1190	0.3121387	10	0	4	16	0

religious_affiliation_general_recoded

XXXXXXX

Distribution

Distribution of values for religious_affiliation_general_recoded

1190 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
religious_affiliation_general_recoded	XXXXXXX	character	1190	0.3121387	3	0	7	9	0

passed_iat_performance

Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms.

Distribution

Distribution of values for passed_iat_performance

122 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
passed_iat_performance	Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms.	logical	122	0.9294798	TRU: 1440, FAL: 168	0.8955224

complete_iat

Complete IAT data (used for exclusions)

Distribution

Distribution of values for complete_iat

122 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
complete_iat	Complete IAT data (used for exclusions)	character	122	0.9294798	3	0	6	8	0

complete_selfreport

Complete self reported evaluations data (used for exclusions)

Distribution

Distribution of values for complete_selfreport

98 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
complete_selfreport	Complete self reported evaluations data (used for exclusions)	character	98	0.9433526	2	0	6	8	0

complete_intentions

Complete behavioural intentions data (used for exclusions)

Distribution

Distribution of values for complete_intentions

1485 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
complete_intentions	Complete behavioural intentions data (used for exclusions)	character	1485	0.1416185	2	0	6	8	0

task_order

Did the particiapant complete the self-reported evaluations or the IAT first?

Distribution

Distribution of values for task_order

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
task_order	Did the particiapant complete the self-reported evaluations or the IAT first?	character	0	1	2	0	9	25	0

iat_block_order

Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?

Distribution

Distribution of values for iat_block_order

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
iat_block_order	Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?	character	0	1	2	0	31	33	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Evaluative learning via deepfaked media",
  "description": "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "creator": "Sean Hughes",
  "citation": "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind.",
  "url": "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-",
  "datePublished": "2020",
  "spatialCoverage": "Online",
  "keywords": ["subject", "experiment", "intervention_medium", "source_valence", "experiment_condition", "exclude_subject", "age", "gender", "IAT_D2", "mean_self_reported_evaluation", "mean_intentions", "deepfake_detected", "diagnosticity", "demand", "reactance", "hypothesis_awareness", "influence_awareness", "aot_actively_openminded_thinking_sum", "bcti_belief_in_conspiracy_sum", "crt_analytic_thinking_sum", "ocq_overclaiming_sum", "ras_relgious_affliation_sum", "rei_rational_sum", "rei_experiential_sum", "me_fake_news_awareness_sum", "me_real_news_awareness_sum", "me_fake_news_accuracy_sum", "me_real_news_accuracy_sum", "me_fake_news_sharing_sum", "me_real_news_sharing_sum", "aot_actively_openminded_thinking_pomp", "bcti_belief_in_conspiracy_pomp", "crt_analytic_thinking_pomp", "ocq_overclaiming_pomp", "ras_relgious_affliation_pomp", "rei_rational_pomp", "rei_experiential_pomp", "me_fake_news_awareness_pomp", "me_real_news_awareness_pomp", "me_fake_news_accuracy_pomp", "me_real_news_accuracy_pomp", "me_fake_news_sharing_pomp", "me_real_news_sharing_pomp", "IAT_D2_recoded_for_source_valence", "mean_self_reported_evaluation_recoded_for_source_valence", "mean_intentions_recoded_for_source_valence", "deepfake_concept_check", "deepfake_detected_rater_1", "deepfake_detected_rater_2", "deepfake_concept_check_rater_1", "deepfake_concept_check_rater_2", "gender_self_describe", "ethnicity", "ethnicity_self_describe", "location", "education", "education_recoded", "employment", "income", "income_recoded", "political_ideology_identity", "political_ideology_social_issues", "political_ideology_economic_issues", "religious_affiliation_general", "religious_affiliation_general_recoded", "passed_iat_performance", "complete_iat", "complete_selfreport", "complete_intentions", "task_order", "iat_block_order"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "subject",
      "description": "Unique subject identifier",
      "@type": "propertyValue"
    },
    {
      "name": "experiment",
      "description": "Experiement data was collected as part of",
      "@type": "propertyValue"
    },
    {
      "name": "intervention_medium",
      "description": "What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio)",
      "@type": "propertyValue"
    },
    {
      "name": "source_valence",
      "description": "What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages?",
      "@type": "propertyValue"
    },
    {
      "name": "experiment_condition",
      "description": "Was the intervention genuine or deepfaked content?",
      "@type": "propertyValue"
    },
    {
      "name": "exclude_subject",
      "description": "Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Participant age",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Participant gender",
      "@type": "propertyValue"
    },
    {
      "name": "IAT_D2",
      "description": "The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks",
      "@type": "propertyValue"
    },
    {
      "name": "mean_self_reported_evaluation",
      "description": "Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_intentions",
      "description": "Mean behavioural intentions to XXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected",
      "description": "Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see \"3 Instructions for raters.docx\"). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.",
      "@type": "propertyValue"
    },
    {
      "name": "diagnosticity",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "demand",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "reactance",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "hypothesis_awareness",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "influence_awareness",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "aot_actively_openminded_thinking_sum",
      "@type": "propertyValue"
    },
    {
      "name": "bcti_belief_in_conspiracy_sum",
      "@type": "propertyValue"
    },
    {
      "name": "crt_analytic_thinking_sum",
      "@type": "propertyValue"
    },
    {
      "name": "ocq_overclaiming_sum",
      "@type": "propertyValue"
    },
    {
      "name": "ras_relgious_affliation_sum",
      "@type": "propertyValue"
    },
    {
      "name": "rei_rational_sum",
      "@type": "propertyValue"
    },
    {
      "name": "rei_experiential_sum",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_awareness_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_awareness_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_accuracy_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_accuracy_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_sharing_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_sharing_sum",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "aot_actively_openminded_thinking_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "bcti_belief_in_conspiracy_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "crt_analytic_thinking_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "ocq_overclaiming_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "ras_relgious_affliation_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "rei_rational_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "rei_experiential_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_awareness_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_awareness_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_accuracy_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_accuracy_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_fake_news_sharing_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "me_real_news_sharing_pomp",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "IAT_D2_recoded_for_source_valence",
      "description": "IAT_D2 recoded for source_valence. If source_valence == \"negative\", IAT_D2*-1, otherwise IAT_D2.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_self_reported_evaluation_recoded_for_source_valence",
      "description": "mean_self_reported_evaluation recoded for source_valence. If source_valence == \"negative\", mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.",
      "@type": "propertyValue"
    },
    {
      "name": "mean_intentions_recoded_for_source_valence",
      "description": "mean_intentions recoded for source_valence. If source_valence == \"negative\", mean_intentions*-1, otherwise mean_intentions.",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected_rater_1",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_detected_rater_2",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check_rater_1",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "deepfake_concept_check_rater_2",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "gender_self_describe",
      "description": "Open ended gender",
      "@type": "propertyValue"
    },
    {
      "name": "ethnicity",
      "description": "Ethnicity",
      "@type": "propertyValue"
    },
    {
      "name": "ethnicity_self_describe",
      "description": "Open ended ethnicity",
      "@type": "propertyValue"
    },
    {
      "name": "location",
      "description": "Location",
      "@type": "propertyValue"
    },
    {
      "name": "education",
      "description": "Education",
      "@type": "propertyValue"
    },
    {
      "name": "education_recoded",
      "description": "Education recoded into XXXXXXXX groups",
      "@type": "propertyValue"
    },
    {
      "name": "employment",
      "description": "Employment status",
      "@type": "propertyValue"
    },
    {
      "name": "income",
      "description": "Income level",
      "@type": "propertyValue"
    },
    {
      "name": "income_recoded",
      "description": "Income level recoded into XXXXX groups",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_identity",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_social_issues",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "political_ideology_economic_issues",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "religious_affiliation_general",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "religious_affiliation_general_recoded",
      "description": "XXXXXXX",
      "@type": "propertyValue"
    },
    {
      "name": "passed_iat_performance",
      "description": "Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms.",
      "@type": "propertyValue"
    },
    {
      "name": "complete_iat",
      "description": "Complete IAT data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "complete_selfreport",
      "description": "Complete self reported evaluations data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "complete_intentions",
      "description": "Complete behavioural intentions data (used for exclusions)",
      "@type": "propertyValue"
    },
    {
      "name": "task_order",
      "description": "Did the particiapant complete the self-reported evaluations or the IAT first?",
      "@type": "propertyValue"
    },
    {
      "name": "iat_block_order",
      "description": "Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?",
      "@type": "propertyValue"
    }
  ]
}`

Write alternative file types to disk

Original csv file is used for analyses (as it is simplest), but other file types that integrate the labels are likely to be more useful for reuse.

I include an R .rds file (which includes data labels and data types), SPSS .sav and Stata .dta files.

write_rds(codebook_data, "processed/4_data_participant_level_with_hand_scoring.rds") 
rio::export(codebook_data, "processed/4_data_participant_level_with_hand_scoring.sav") # SPSS file
rio::export(codebook_data, "processed/4_data_participant_level_with_hand_scoring.dta") # Stata file

Session Info

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rio_0.5.16       labelled_2.7.0   codebook_0.9.2   kableExtra_1.3.1
##  [5] knitr_1.30       forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2     
##  [9] purrr_0.3.4      readr_1.3.1      tidyr_1.1.2      tibble_3.0.3    
## [13] ggplot2_3.3.2    tidyverse_1.3.0 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.1        jsonlite_1.7.1    viridisLite_0.3.0 modelr_0.1.8     
##  [5] assertthat_0.2.1  highr_0.8         cellranger_1.1.0  yaml_2.2.1       
##  [9] globals_0.13.1    pillar_1.4.6      backports_1.1.9   glue_1.4.2       
## [13] digest_0.6.25     rvest_0.3.5       colorspace_1.4-1  htmltools_0.5.0  
## [17] pkgconfig_2.0.3   broom_0.7.2       listenv_0.8.0     haven_2.3.1      
## [21] scales_1.1.1      webshot_0.5.2     openxlsx_4.1.5    generics_0.0.2   
## [25] farver_2.0.3      ellipsis_0.3.1    DT_0.13           withr_2.2.0      
## [29] repr_1.1.0        skimr_2.1.2       cli_2.0.2         rmdpartials_0.5.8
## [33] magrittr_1.5      crayon_1.3.4      readxl_1.3.1      evaluate_0.14    
## [37] fs_1.4.1          future_1.19.1     fansi_0.4.1       xml2_1.3.2       
## [41] foreign_0.8-80    tools_4.0.2       data.table_1.13.2 hms_0.5.3        
## [45] lifecycle_0.2.0   munsell_0.5.0     reprex_0.3.0      zip_2.1.1        
## [49] compiler_4.0.2    rlang_0.4.8       grid_4.0.2        rstudioapi_0.11  
## [53] htmlwidgets_1.5.1 crosstalk_1.1.0.1 base64enc_0.1-3   labeling_0.3     
## [57] rmarkdown_2.5     gtable_0.3.0      codetools_0.2-16  DBI_1.1.0        
## [61] curl_4.3          R6_2.4.1          lubridate_1.7.9   stringi_1.4.6    
## [65] parallel_4.0.2    Rcpp_1.0.5        vctrs_0.3.4       dbplyr_1.4.3     
## [69] tidyselect_1.1.0  xfun_0.15